Leveraging Machine Learning to Mitigate Multipath in a GNSS Pure L5 Receiver

Mahdi Maaref, Lionel Garin, and Paul McBurney

Abstract: A Machine Learning (ML)-based framework for navigating with global navigation satellite system (GNSS) signals in urban environments is developed. This framework aims to incorporate a pure L5 navigation system to obviate the requirement for dual frequency front-end. To this end, first, this paper quantifies the performance of a pure L5 receiver in static and dynamic heavy multipath signal environment. Then, a deep neural network (DNN)-based methodology to leverage ML to mitigate multipath is presented. The performance of the proposed framework is analyzed. Experimental results for a dynamic receiver navigating in a deep urban environment show that the proposed framework reduces the 95% horizontal confidence level from 44.0 m to 18.8 m. It is also shown that the proposed framework is able to reduce the standard deviation of the pseudorange error from 11.22 m to 5.34 m.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 3740 - 3748
Cite this article: Maaref, Mahdi, Garin, Lionel, McBurney, Paul, "Leveraging Machine Learning to Mitigate Multipath in a GNSS Pure L5 Receiver," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 3740-3748.
https://doi.org/10.33012/2021.18014
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